We pass invalid pointers when we check the size of the required
compute graph before fitting. Some CUDA APIs validate these pointers
but we can just skip them during this phase. cudaMemsetAsync is one
of these that we weren't skipping but never took the code path that
used it. Now that we have enabled op_offload, we can hit it in
memory pressured situations.
When a model is partially offloaded to system RAM, we can either
do the calculations on the CPU or we can temporarily transfer the
data to the GPU to do the calculations there. Small batches tend
to be better on the CPU, large batches on the GPU.
The llamarunner used the GPU in most cases and the ollamarunner
used the CPU. Although the ollamarunner saw an improvement in
token generation performance, there was a large performance hit
in prompt processing (3-10x).
There is an existing heuristic to dynamically switch between these
two modes but in practice it doesn't have enough information to
accurately make that decision. This adds authoritative data to make
the check work to get the best of both worlds.
Fixes#12037
* Fix vulkan PCI ID and ID handling
Intel GPUs may not report PCI IDs which was leading to incorrect overlap
detection. Switch to using the existing PCI IDs, however AMD GPUs claim not to
report PCI IDs, but actually do, so try anyway, as this is required for ADLX to
find the GPUs on Windows. Numeric IDs lead to scheduling problems, so this also
switches Vulkan to use UUID based IDs. The GPU discovery patches have been
squashed into a single patch to simplify future rebases.
* review comments
Users on Windows without GPUs are reporting errors relating to
cudaDriverGetVersion with the device set to -1. This ensures we only grab the
driver once we're enumerating actual devices.
When loading the dynamic libraries, if something goes wrong report some
details. Unfortunately this wont explain which dependencies are missing,
but this breadcrumb in the logs should help us diagnose GPU discovery
failures.
* Simplify NVML fallback for unified memory GPUs
Remove device-specific checks and environment variable dependency for
NVML_ERROR_NOT_SUPPORTED fallback. When NVML doesn't support memory
queries, unconditionally use /proc/meminfo instead of checking device
names or OLLAMA_UNIFIED_MEMORY environment variable.
This provides better memory reporting by using MemAvailable which
accounts for reclaimable memory, avoiding the underreporting issue
described in NVIDIA support article a_id/5728.
Tested on NVIDIA GB10 unified memory iGPU with consistent and accurate
memory reporting across multiple model load/unload cycles.
* Add NVML fallback patch for unified memory GPUs
* implement the vulkan C backend
* add support in gpu.go
* add support in gen_linux.sh
* it builds
* fix segfault
* fix compilation
* fix free memory monitor
* fix total memory monitor
* update gpu.go
* fix build
* fix check_perfmon len
* remove cap_get_bound check
* fix vulkan handle releasing
* fix build on federa 40
* fix vulkan on windows
* making amdgpu work on arm achitecutre with vulkan
* add x86_64 lines in VulkanGlobs and capLinuxGlobs
* add aarch64 lines in vulkanGlobs and capLinuxGlobs
* Fix variable name
* Add vulkan build patch from @jmorganca
* Sync vendored ggml to add Vulkan support
* Updated dockerfile
https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Installing rocm library
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* This version works well
built based on this: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Applied 00-fix-vulkan-building.patch
Work done by McBane87 here: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Fixed the "detached head" issues
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Merged in the right direction
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Merging the latest stable (#2)
* Applied 00-fix-vulkan-building.patch
* Implemented vulkan backend based on the work done by whyvl, Dts0, McBane87 and others
Tested on AMD Ryzen 7 8845HS w/ Radeon 780M Graphics with ROCm disabled
```
[GIN-debug] POST /v1/chat/completions --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (6 handlers)
[GIN-debug] POST /v1/completions --> github.com/ollama/ollama/server.(*Server).GenerateHandler-fm (6 handlers)
[GIN-debug] POST /v1/embeddings --> github.com/ollama/ollama/server.(*Server).EmbedHandler-fm (6 handlers)
[GIN-debug] GET /v1/models --> github.com/ollama/ollama/server.(*Server).ListHandler-fm (6 handlers)
[GIN-debug] GET /v1/models/:model --> github.com/ollama/ollama/server.(*Server).ShowHandler-fm (6 handlers)
time=2025-03-11T13:00:40.793Z level=INFO source=gpu.go:199 msg="vulkan: load libvulkan and libcap ok"
time=2025-03-11T13:00:40.877Z level=INFO source=gpu.go:421 msg="error looking up vulkan GPU memory" error="device is a CPU"
time=2025-03-11T13:00:40.878Z level=WARN source=amd_linux.go:443 msg="amdgpu detected, but no compatible rocm library found. Either install rocm v6, or follow manual install instructions at https://github.com/ollama/ollama/blob/main/docs/linux.md#manual-install"
time=2025-03-11T13:00:40.878Z level=WARN source=amd_linux.go:348 msg="unable to verify rocm library: no suitable rocm found, falling back to CPU"
time=2025-03-11T13:00:40.879Z level=INFO source=types.go:137 msg="inference compute" id=0 library=vulkan variant="" compute=1.3 driver=1.3 name="AMD Radeon Graphics (RADV GFX1103_R1)" total="15.6 GiB" available="15.6 GiB"
```
```
# ollama run phi4:14b
>>> /set verbose
Set 'verbose' mode.
>>> how's it going?
Hello! I'm here to help you with any questions or tasks you have. How can I assist you today? 😊
total duration: 3.341959745s
load duration: 18.165612ms
prompt eval count: 15 token(s)
prompt eval duration: 475ms
prompt eval rate: 31.58 tokens/s
eval count: 26 token(s)
eval duration: 2.846s
eval rate: 9.14 tokens/s
>>>
```
* This is no longer needed
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Fixes SIGSEGV: segmentation violation running gemma3 models on ollama 0.6.0 #21
Patch provided by McBane87 on https://github.com/whyvl/ollama-vulkan/issues/21
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Applied 04-disable-mmap-vulkan.patch
From: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2660836871
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Pulled new upstream code for ggml-bulkan backend
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Merged latest ollama 0.6.2 and nasrally's Flash Attention patches (#5)
* readme: add Ellama to list of community integrations (#9800)
* readme: add screenpipe to community integrations (#9786)
* Add support for ROCm gfx1151 (#9773)
* conditionally enable parallel pipelines
* sample: make mutations in transforms explicit (#9743)
* updated minP to use early exit making use of sorted tokens
* ml/backend/ggml: allocate memory with malloc when loading model (#9822)
* runner: remove cache prompt flag from ollama runner (#9826)
We do not need to bypass the prompt caching in the ollama runner yet, as
only embedding models needed to bypass the prompt caching. When embedding
models are implemented they can skip initializing this cache completely.
* ollamarunner: Check for minBatch of context space when shifting
Models can specify that a group of inputs need to be handled a single
batch. However, context shifting didn't respect this and could trigger
a break anyways. In this case, we should instead trigger a context
shift earlier so that it occurs before the grouped batch.
Note that there still some corner cases:
- A long prompt that exceeds the context window can get truncated
in the middle of an image. With the current models, this will
result in the model not recognizing the image at all, which is
pretty much the expected result with truncation.
- The context window is set less than the minimum batch size. The
only solution to this is to refuse to load the model with these
settings. However, this can never occur with current models and
default settings.
Since users are unlikely to run into these scenarios, fixing them is
left as a follow up.
* Applied latest patches from McBane87
See this for details: https://github.com/whyvl/ollama-vulkan/issues/7#issuecomment-2708820861
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
* Add ability to enable flash attention on vulkan (#4)
* discover: add flash attention handling for vulkan
* envconfig: fix typo in config.go
As part of the process some code was refactored and I added a new field
FlashAttention to GpuInfo since the previous solution didn't allow for a
granular check via vulkan extensions. As a side effect, this now allows
for granular per-device FA support checking in other places
---------
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
Co-authored-by: zeo <108888572+zeozeozeo@users.noreply.github.com>
Co-authored-by: Louis Beaumont <louis.beaumont@gmail.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
Co-authored-by: Michael Yang <mxyng@pm.me>
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Nikita <50599445+nasrally@users.noreply.github.com>
* Revert Readme changes
* Revert
* Revert changes in amd_linux.go
* Revert changes in amd_linux.go
* Remove flashattention setting gpu.go
* Revert whitespace changes in gpu.go
* Revert changes in transforms_test.go
* Revert changes in runner.go
* Revert changes in Makefile.sync
* Revert some unintented changes in Dockerfile
* Revert vulkan copy changes in Dockerfile
* Update Vulkan Code to de4c07f93783a1a96456a44dc16b9db538ee1618
* Fixed duplicate sync in ggml.go
* Revert changes in ggml.go
* Revert chnages in ggml.go
* enable falsh attention on vulkan
* revert remove parenthesis
* fixed flash attention logic enabling
* vk_check_flash_attention 0 means supported
* Update gpu.go
* Add vulkan to Windows Build script
* Remove commented out code
* Enable Vulkan Flash attention in FlashAttentionSupported
* Fix logging
* Update Vulkan backend to e54d41befcc1575f4c898c5ff4ef43970cead75f
* Removed libcap related code
libcap is not directly related to Vulkan and should be added by its own PR. It adds additional library dependencies for building and also requires users to run setcap or run ollama as root, which is not ideal for easy use
* Fix Unit Test (Add Vulkan Library)
* Add vulkan to TestHomogeneousGPUs
Test
* vulkan: get GPU ID (ollama v0.11.5)
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
* disable mmap for vulkan
* Reduce Changes remove TestHomogeneousGPUs (doesn't exist on master)
* Update vulkan version to the version used in llama.cpp
* rename gpu patch to correct number
* added Vulkan API to get correct Device UUID
current UUID from pipelineCacheUUID does not match CUDA
* Fix GPU ID Patch
* Remove Code not in llama.cpp
* modified UUID code inside ggml
* Fix Patch
* Copied minimal definition from vulkan header
* Fix compile error in Mac
Metal is preferred so we're disabling Vulkan for now
* Removed unused code
Fix linter error in CI
* Fix patches apply
* fixing lint error
* Removed unneeded function call
Somehow removing this call fixed the crashing when Vulkan header was removed
* added missing NL
* Fixed missing members in Vulkan header
also added zero clear for some structs
* Fixed wrong structure ID
* Fixed Vulkan header
More aligned with official header definition now
* buildvulkanAsSeperateFunction
* Vulkan on Windows Test
* temporarly comment out gate to run windows task
* use temporarly windows-latest for build
* Commenting out other presets to build vulkan
* reenable cpu
* commenting out error action stop
* temporarly commenting out rocm
* set vulkan path
* comment out cude for faster turnaround
* correct vulkan install
* correct vulkan silent install
* fixed install command
* revert debugging changes (vulkan builds on windows)
* revert windows-latest
* trying to build vulkan for linux
* temporarly disable cuda and rocm
* try again linux build
* fix version
* trying to fix
* trying again
* trying again
* fix version
* fixed vulkan-sdk name
* try again
* trying again
* try without version number
* try again
* add some more extra
* trying to use version 1.4.313
* revert debugging changes
* Filter out already supported gpus
* revert debug code
* Use runners for GPU discovery
This revamps how we discover GPUs in the system by leveraging the Ollama
runner. This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs. Now the runner does that implicitly based on the actual
device list. In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.
Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.
Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.
* timing info for runner
* WIP - wire up Vulkan with the new engine based discovery
Not a complete implementation - free VRAM is better, but not accurate on
windows
* fix - trust the library paths from discovery when starting runner
* fix index bug
* fix vulkan ids to be underlying
* fix - give bootstrapping more time on slow systems
* Test if Vulkan device is supported
* vk_check_flash_attention is not needed (coompat2 coopmapt and scalar implementation exist)
* Handle GGML_VK_VISIBLE_DEVICES
* ask for supported first
* win: fix CPU query buffer handling
Try in a short loop until we get the size right.
* test: harden integration tests for slow start
If the server takes a while to start up, block
tests from starting until it's online to avoid
setting large timeouts in individual test cases.
* gofumpt fix
* fix build
* merge fixes
* merge fixes
* fixed build
* merge fixes
* fixing build
* fixed build
* fixed formatting
* fixed build
* fix vulkan gpu id patch
* sync llama.cpp vulkan code
* update build windows script
* merge fixes
* fix format
* fixed vulkan casing
* handle igpu as gpu
* improve case
* print out unknown library
* rturn Vulkan for vulkan library
* Revert "rturn Vulkan for vulkan library"
This reverts commit 690461a12f.
* fixed patch number
* return Library Name
* remvoe debug code
* return integrated in vulkan backend
* Return pci Properties
* update patch
* directly get pci proeprties without parsing
* workaround for filtering devices. Correct way is to have a LibraryPosition Parameter in the deviceInfo
* Revert "directly get pci proeprties without parsing"
This reverts commit 8e0624851f.
* Set FilteredID for Environment Filtering
* ROCm Library is named ROCm
* revert changes in patch
* Create 0028-vulkan-pci-and-memory.patch
* vulkan memory patch
* casing fix
* Add more pci properties
* Added better memory management
* Added better memory managament
* fixed patch
* Fixed patch
* FilterID creation group by library
* filter out vulkan supported by other gpu
* fixing deviceid compare
* Vulkan Fix FA coopmat1 invalid array indexing
* Use everywhere the same Vulkan Version 1.4.321.1
* Remove unneeded patch
* vulkan update
* sync vulkan glsl files
* only use for vulkan the filteredid (numeric device number)
* simplify code
---------
Signed-off-by: Vadim Grinco <vadim@grinco.eu>
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: pufferffish <github@bandersnatch.anonaddy.com>
Co-authored-by: KOISHI KOMEIJI FROM TOUHOU 11 <fuck>
Co-authored-by: DSLstandard <qgeneral35@gmail.com>
Co-authored-by: pufferffish <me@windtfw.com>
Co-authored-by: yeongbba <yeongmo.lee@logpresso.com>
Co-authored-by: tomaThomas <tomathomas@mailbox.org>
Co-authored-by: Antoine Viallon <antoine@lesviallon.fr>
Co-authored-by: Vadim Grinco <vadim@grinco.eu>
Co-authored-by: zeo <108888572+zeozeozeo@users.noreply.github.com>
Co-authored-by: Louis Beaumont <louis.beaumont@gmail.com>
Co-authored-by: Daniel Hiltgen <dhiltgen@users.noreply.github.com>
Co-authored-by: Michael Yang <mxyng@pm.me>
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Nikita <50599445+nasrally@users.noreply.github.com>
Co-authored-by: Masato Nakasaka <masato.nakasaka@intel.com>
Co-authored-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
* feat: Bump llama.cpp to df1b612
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(mtmd): Correctly encode text chunks during mtmd tokenization
There can be text chunks that appear interspersed with the image embeddings
that contain template delimiter tokens for some models. These need to be
correctly translated to text tokens.
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* tests: Use MtmdChunk in image_test
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Fix unnecessary conversion linting
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(ggml): Revert changes to ggml_hip.cpp
These changes were done largely by our code assistant and are likely wrong
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Revert changes in mem_nvml.cpp
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update sync point to 1deee0
This brings in several more optimization commits and model support for
EmbeddingGemma
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches for 1deee0
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: sync for bump to 1deee0
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Bad patch updates with errant `+`
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Bump llama.cpp/ggml to 7049736
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: format-patches after latest bump
Branch: LlamaCPPBump-GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This revamps how we discover GPUs in the system by leveraging the Ollama
runner. This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs. Now the runner does that implicitly based on the actual
device list. In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.
Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.
Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.
The GGML CUDA backend allocates additional memory for intermediate
results during calculation. This memory isn't currently allocated
during worst case graph reservation and therefore not included in
scheduling. This means that as these buffers potentially grow
with context length, we could crash.
This extends the memory allocation system down layer from the GGML
graph to the CUDA layer, preallocating the worst case memory there
as well.
Fixes#11753
The GGML scale kernel uses signed 32-bit ints to represent
the number of elements in the tensor. For large images,
mistral-small3.2 overflows this, triggering CUDA errors due
to negative arguments.
Currently, this can happen when the user passes a large image
to mistral-small3.2. However, with upcoming changes to reserve
CUDA memory, it happens every time mistral-small is loaded as
we reserve using a worst case batch.
This patch is part of an upstream GGML commit and should be removed
after GGML is updated past 0a1b398 "ggml: add ops for WAN video model
(cuda && cpu) (#15669)".
Fixes#10388
For each memory allocation we report the size of the (attempted)
allocation and whether it succeeded or failed. The latter status
reporting proved to be not that useful in practice as systems
such as Windows can automatically overflow from VRAM into RAM,
resultings in successful allocations even when there isn't
enough memory where we wanted.
As a result, this information is only used for debug logging,
which isn't worthwhile enough for the amount of code. It
also isn't fully accurate, as multiple allocations may result
in partial failures.
* perf: build graph for next batch in parallel to keep GPU busy
This refactors the main run loop of the ollama runner to perform the main GPU
intensive tasks (Compute+Floats) in a go routine so we can prepare the next
batch in parallel to reduce the amount of time the GPU stalls waiting for the
next batch of work.
* tests: tune integration tests for ollama engine
This tunes the integration tests to focus more on models supported
by the new engine.
The recent memory management changes caused all GPUs to be visible
to the runner, regardless of whether they are ultimately used. This
caused CUDA devices to allocate a primary context (~300 MB VRAM) on
each GPU, for each model. This is unnecessary, so we can both avoid
touching GPUs that we exclude in the early stage of allocation and
freeing the memory for any that we touch but don't use.
The issue will continue to exist for the old engine, since it touches
all devices during initialization.
Flash attention kernels require the mask of the KV cache be a F16
rather than an F32. We can use the GGML operation ggml_cast to do
this rather than doing it ourselves, which allows reuse of a
preallocated buffer in the graph rather than allocating a new one
for each batch. This improves token generation performance with
flash attention by 10-30% (with gpt-oss). This also makes performance
with flash attention better than without it, as expected.
This changes the memory allocation strategy from upfront estimation to
tracking actual allocations done by the engine and reacting to that. The
goal is avoid issues caused by both under-estimation (crashing) and
over-estimation (low performance due to under-utilized GPUs).
It is currently opt-in and can be enabled for models running on the
Ollama engine by setting OLLAMA_NEW_ESTIMATES=1. Behavior in other
cases is unchanged and will continue to use the existing estimates.
* TEMPORARY: Update the llama.cpp upstream to my fork's Granite Four branch
This will be redone once my branch is merged upstream in llama.cpp
* feat: Update all patches
There are a number that are no longer needed at all:
- 0003-embeddings: Embeddings entirely overhauled on master
- 0008-ensure-KV-cache-is-fully-defragmented: KV caching entirely
overhauled on master
- 0019-metal-add-mean-kernel-14267: Merged upstream
- 0020-CUDA-add-mean-operation-14313: Merged upstream
* feat: Sync llama.cpp and ggml
* fix: Update rsync-filter for all moved/new/removed files
* fix: Add files missing from sync
* fix: Update ggml rsync-filter for new ggml-cpu/arch subdirs
* fix: Add ggml files missing from sync
* fix: Narrow llama.cpp rsync-filter to not include mtmd main tool cpp files
* fix: Remove mtmd main cpp files
* fix: Add missing include in sampling_ext.cpp
* fix: Update llama.go to use mtmd instead of clip/llava
* fix: Add patch for mtmd_input_text
* chore: Ignore *.patched in the patch directory
* fix: Fix support for arch-specific ggml-cpu source files with new arrangement
In https://github.com/ggml-org/llama.cpp/pull/13892, all arch-specific
implementations were split out into a nested tree structure under
ggml-cpu/arch. This conflicts with standard CGO layout where all
arch-specific source files are expected to live in the same directory as
the parent go module and use suffixes based on GOOS and GOARCH. As such,
there were really two options for getting this to work:
1. Add a patch on top of the GGML sync to rearrange the files to match the
GO layout convention
2. Use CGO directives to conditionally include the nested source files in
the compilation units
This commit does (2) in order to minimize the set of changes needed on top
of the upstream file layout. To get this to work, there are two key things
needed:
1. In cpu.go, #cgo directives are added to explicitly set __${GOARCH}__ in
the preprocessor directives
2. In arch-impls.c|cpp, use an #ifdef | #elif defined | #endif chain to
explicitly include the .c|.cpp files for the given architecture from the
nested directory
* fix: Use mtmd_helper to correctly load the bitmap for the image
* fix: Apply patch for mtmd_text_input
* fix: Add missing stb to llama.cpp rsync-filter
* fix: Add sync'ed stb vendored header
* fix: Use c++17 and include vendor for go wrapper modules
* fix: Update patch 0015 for upstream implementation of uuid
* feat: Bump to the latest tip of the branch
* fix: Update patches for bump
* feat: Bump back to the cenral repo and point at the latest master
This includes granite 4 and a number of other model architectures!
* fix: Revert changes to ggml export GPU UUID patch
* fix: Add patch for GGML_VERSION and GGML_COMMIT constants
* feat: Sync all patched code
* build: Include cmake/common.cmake in ggml sync
* build: Add top-level include for GNUINstallDirs in CMakeLists.txt
This is used to populate CMAKE_INSTALL_BINDIR
* fix: Add a patch to avoid power throttling API on non-msvc windows builds
* fix: Sync patch changes for ggml-cpu.c
* feat: Bump llama.cpp to 4a4f42
This picks up support for Kimi K2 and PLaMO-2
* feat: Sync llama.cpp
* fix: Handle multi-chunk image encodings from mtmd
* fix: Re-number patches after merge with `main`
* feat: Bump to 41e78c in the makefile
* fix: Fix Solar and argsort/copy patches after bump
* fix: Remove Gemma3n CUDA Graphs patch
It was implemented upstream:
https://github.com/ggml-org/llama.cpp/pull/14741
* feat: Sync llama.cpp / ggml after latest bump
* build: Remove unnecessary CFLAGS definitions in cpu.go
* fix: Remove unnecessary additions in the rsync-filter
* fix: Remove unused vendored code for chat template parsing
* Revert "fix: Remove Gemma3n CUDA Graphs patch"
This reverts commit d724caced3.
* fix: Update 0020 CUDA Graphs for gemma3n to keep both llama.cpp and ollama fixes
https://github.com/ollama/ollama/pull/11195#issuecomment-3137312394
* fix: Sync ggml-cuda.cu after keeping both style cuda graph fixes for gemma3n
* unwind mxfp4 patch
Prepare to bump ggml with their impl for mxfp4
* bump
* fix windows build error
* Convert tensors at load time
Repack the mxfp4 tensors as ggmls kernels expect them to be.
* convert mlp bf16 to f32
* buffer the conversion better
* reshape earlier
* openai swiglu
* add ids
* split qkv, gate_up
* fix nested alt tags
* fast attention
* remove debug messages
* fix lint
* remove redundant test
* remap values only if source/target are different
* add back i32->i32 copy
* refactor cpu quants
* clean up vendor
* update patch instructions
* clean up patches
* remove webgpu
* update mem
* also handle gpt-oss
* revert convert changes
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Some AMD GPUs do not provide UUIDs and report only "XX". In these
cases, we should use the ordinal ID as an alternate identifier.
This is the same as we always need to do on Windows for AMD.
In addition, this prints out the ID for each GPU when enumerating
them for easier debugging in the future.
Callers can set a backend buffer type to be no-alloc, meaning that
it does not allocate memory for tensors or operations. This can
be used for calculating memory requirements. Tensors and graphs
must be recreated with no-alloc set to false before loading data.
Defaults to false for newly created backend buffer types.
For many backend data structures, GGML defines a typedef of a pointer
type and returns these from functions. In most cases, CGo understands
that these are interchangable but some parts of Go (such as generics)
think they are two different types. We should prefer the form that
GGML uses.
* bf16
* tests
* gpt-oss
* enable gptoss for engine
* rough estimate
* convert to mxfp4
* handle safetensors U8
* clamp glu/linear
* update tokenizer
* MXFP4 support
This implements the Open Compute Microscaling (MX) FP4 format
as a tensor type with backend implementations focusing
on mulmat and mulmatid on CPU, CUDA, and Metal.
* Unit tests for MXFP4 support
This exercises various operations and shapes on both CPU and GPU (if detected
on the system)
* cuda graph
* unit test adjustments
* cuda: optimize memory access
Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4
* mac: fix crash on old macos versions
cblas_sgemm is only supported on v13.3 and up, however bf16 is
only supported on v14+ so we were falling back to ggml-blas and
crashing on bf16 tensors. Checking for the function being null
seems to be the simplest way to condittionally avoid registering the
backend.
* server: Minimum context length for gptoss
This model requires a minimum context length of 8192 to function
effectively. Users can set higher values through all normal mechanisms
but lower values will be silently reset.
* ggml: Multiply by numParallel for gptoss sliding window
When computing the graph size estimate, the context size is already
multiplied by numParallel so estimates reflect that. However, since
sliding window models use a smaller, fixed context size, they need
to manually take numParallel into account.
* gpt-oss integration
includes harmony parser and thinking levels, etc.
* fix sync
* fix tests
* fix lint
---------
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
* Enable CUDA Graphs for gemma3n.
Similar to
https://github.com/ggml-org/llama.cpp/pull/14741,
though ollama has a slightly different model graph
than llama.cpp which requires different workaround
checks.
* Remove residual check by reshaping differently in gemma3n model
This should make the heuristics more robust
Reporting params.NumGPULayers can be misleading because it is the
requested number of layers, not the actual number that is loaded.
While they are often the same, there are cases where they might mismatch,
such as if the GPU backend is missing.
We don't get valid UUIDs for AMD GPUs on Windows, so the best option
is to use the ordinal IDs. This brings us in line with what we currently
do on the Ollama server - the only exception is AMD GPUs on Linux, which
falls back to using ordinal IDs. The GGML implementation has no fallback
but it doesn't appear to occur for any of the GPUs that we support.
It's also possible that there are collisions between ordinal IDs for
different libraries - however the only places where we use them are
AMD on Windows and Metal on Mac, which can never occur on the same
system.
This is causing segfaults, so disable it. Currently UUIDs are only
used for debugging purposes, although they planned to be used in
additional ways in the future.
Bug #11211
